




import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


lowerCamelCase__            = ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCamelCase__            = {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
lowerCamelCase__            = logging.get_logger(__name__)

lowerCamelCase__            = ''' Hello world! cécé herlolip'''

lowerCamelCase__            = [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Union[str, Any] ):






	"""simple docstring"""
	snake_case__						:     Tuple		=[
	    '''encoder.version''',
	    '''decoder.version''',
	    '''model.encoder.version''',
	    '''model.decoder.version''',
	    '''_float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(UpperCamelCase_						,						UpperCamelCase_ )







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		List[str]						,						SCREAMING_SNAKE_CASE						:		str						,						SCREAMING_SNAKE_CASE						:		Union[str, Any] ):






	"""simple docstring"""
	snake_case__						:     Optional[int]		=dct.pop(UpperCamelCase_ )
	snake_case__						:     Any		=val







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		List[str] ):






	"""simple docstring"""
	snake_case__						:     Union[str, Any]		=torch.load(UpperCamelCase_						,						map_location='''cpu''' )
	snake_case__						:     Any		=torch.hub.load('''pytorch/fairseq'''						,						'''bart.large.cnn''' ).eval()
	hub_interface.model.load_state_dict(sd['''model'''] )
	return hub_interface







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		List[str] ):






	"""simple docstring"""
	snake_case__,    snake_case__						:     Optional[Any]		=emb.weight.shape
	snake_case__						:     Optional[int]		=nn.Linear(UpperCamelCase_						,						UpperCamelCase_						,						bias=UpperCamelCase_ )
	snake_case__						:     Any		=emb.weight.data
	return lin_layer







@torch.no_grad()
def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		int						,						SCREAMING_SNAKE_CASE						:		Any						,						SCREAMING_SNAKE_CASE						:		Any=None ):






	"""simple docstring"""
	if not os.path.exists(UpperCamelCase_ ):
		snake_case__						:     List[Any]		=torch.hub.load('''pytorch/fairseq'''						,						UpperCamelCase_ ).eval()
	else:
		snake_case__						:     Dict		=load_xsum_checkpoint(UpperCamelCase_ )

	bart.model.upgrade_state_dict(bart.model.state_dict() )
	if hf_checkpoint_name is None:
		snake_case__						:     Any		=checkpoint_path.replace('''.'''						,						'''-''' )
	snake_case__						:     List[Any]		=BartConfig.from_pretrained(UpperCamelCase_ )
	snake_case__						:     str		=bart.encode(UpperCamelCase_ ).unsqueeze(0 )
	snake_case__						:     str		=BartTokenizer.from_pretrained(UpperCamelCase_ ).encode(UpperCamelCase_						,						return_tensors='''pt''' ).unsqueeze(0 )
	if not torch.eq(UpperCamelCase_						,						UpperCamelCase_ ).all():
		raise ValueError(
		    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

	if checkpoint_path == "bart.large.mnli":
		snake_case__						:     str		=bart.state_dict()
		remove_ignore_keys_(UpperCamelCase_ )
		snake_case__						:     Optional[int]		=state_dict['''model.decoder.embed_tokens.weight''']
		for src, dest in mnli_rename_keys:
			rename_key(UpperCamelCase_						,						UpperCamelCase_						,						UpperCamelCase_ )
		snake_case__						:     Any		=BartForSequenceClassification(UpperCamelCase_ ).eval()
		model.load_state_dict(UpperCamelCase_ )
		snake_case__						:     Tuple		=bart.predict('''mnli'''						,						UpperCamelCase_						,						return_logits=UpperCamelCase_ )
		snake_case__						:     str		=model(UpperCamelCase_ )[0]  # logits
	else:  # no classification heads to worry about
		snake_case__						:     Optional[Any]		=bart.model.state_dict()
		remove_ignore_keys_(UpperCamelCase_ )
		snake_case__						:     str		=state_dict['''decoder.embed_tokens.weight''']
		snake_case__						:     str		=bart.extract_features(UpperCamelCase_ )
		if hf_checkpoint_name == "facebook/bart-large":
			snake_case__						:     int		=BartModel(UpperCamelCase_ ).eval()
			model.load_state_dict(UpperCamelCase_ )
			snake_case__						:     str		=model(UpperCamelCase_ ).model[0]
		else:
			snake_case__						:     Union[str, Any]		=BartForConditionalGeneration(UpperCamelCase_ ).eval()  # an existing summarization ckpt
			model.model.load_state_dict(UpperCamelCase_ )
			if hasattr(UpperCamelCase_						,						'''lm_head''' ):
				snake_case__						:     int		=make_linear_from_emb(model.model.shared )
			snake_case__						:     Union[str, Any]		=model.model(UpperCamelCase_ )[0]

			 # Check results
	if fairseq_output.shape != new_model_outputs.shape:
		raise ValueError(
		    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
	if (fairseq_output != new_model_outputs).any().item():
		raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`''' )
	Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
	model.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
		lowerCamelCase__            = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
		)
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument(
		    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
		)
		lowerCamelCase__            = parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_SCREAMING_SNAKE_CASE 		=      get_tests_dir("""fixtures""")
_SCREAMING_SNAKE_CASE 		=      get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
_SCREAMING_SNAKE_CASE 		=      get_tests_dir("""fixtures/dummy-config.json""")
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      0







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""				)
    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				)
    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCamelCase			  =      WavaVecaConfig()

     # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				).to_dict()

     config_dict.pop("""feature_extractor_type"""				)
     UpperCamelCase			  =      WavaVecaFeatureExtractor(**lowerCamelCase_				)

     # save in new folder
     model_config.save_pretrained(lowerCamelCase_				)
     config.save_pretrained(lowerCamelCase_				)

     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				)

     # make sure private variable is not incorrectly saved
     UpperCamelCase			  =      json.loads(config.to_json_string()				)
     self.assertTrue("""_processor_class""" not in dict_as_saved				)

    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				)
    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    with self.assertRaisesRegex(
        lowerCamelCase_   ,   """bert-base is not a local folder and is not a valid model identifier"""				):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained("""bert-base"""				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    with self.assertRaisesRegex(
        lowerCamelCase_   ,   R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_   ,   revision="""aaaaaa"""				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    with self.assertRaisesRegex(
        lowerCamelCase_   ,   """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""   ,   ):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    with self.assertRaises(lowerCamelCase_				):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""				)
    # If remote code is disabled, we can't load this config.
    with self.assertRaises(lowerCamelCase_				):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""   ,   trust_remote_code=lowerCamelCase_				)

    UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
        """hf-internal-testing/test_dynamic_feature_extractor"""   ,   trust_remote_code=lowerCamelCase_				)
    self.assertEqual(feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)

    # Test feature extractor can be reloaded.
    with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(lowerCamelCase_				)
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_   ,   trust_remote_code=lowerCamelCase_				)
    self.assertEqual(reloaded_feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    try:
     AutoConfig.register("""custom"""   ,   lowerCamelCase_				)
     AutoFeatureExtractor.register(lowerCamelCase_   ,   lowerCamelCase_				)
     # Trying to register something existing in the Transformers library will raise an error
     with self.assertRaises(lowerCamelCase_				):
      AutoFeatureExtractor.register(lowerCamelCase_   ,   lowerCamelCase_				)

     # Now that the config is registered, it can be used as any other config with the auto-API
     UpperCamelCase			  =      CustomFeatureExtractor.from_pretrained(lowerCamelCase_				)
     with tempfile.TemporaryDirectory() as tmp_dir:
      feature_extractor.save_pretrained(lowerCamelCase_				)
      UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				)
      self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)

    finally:
     if "custom" in CONFIG_MAPPING._extra_content:
      del CONFIG_MAPPING._extra_content["custom"]
     if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
      del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
       __lowerCAmelCase           =				True

    try:
     AutoConfig.register("""custom"""   ,   lowerCamelCase_				)
     AutoFeatureExtractor.register(lowerCamelCase_   ,   lowerCamelCase_				)
     # If remote code is not set, the default is to use local
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""				)
     self.assertEqual(feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)
     self.assertTrue(feature_extractor.is_local				)

     # If remote code is disabled, we load the local one.
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""   ,   trust_remote_code=lowerCamelCase_				)
     self.assertEqual(feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)
     self.assertTrue(feature_extractor.is_local				)

     # If remote is enabled, we load from the Hub
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""   ,   trust_remote_code=lowerCamelCase_				)
     self.assertEqual(feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)
     self.assertTrue(not hasattr(lowerCamelCase_   ,   """is_local"""				)				)

    finally:
     if "custom" in CONFIG_MAPPING._extra_content:
      del CONFIG_MAPPING._extra_content["custom"]
     if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
      del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
